A robust time-dependent model of alkali-silica reaction at different temperatures

Alkali-Silica-Reaction (ASR) is one of the most deteriorating phenomena in concrete structures. This study uses a machine learning approach (i.e. Artificial Neural Network) to provide further insight into ASR. The approach combines chemo-mechanical and kinetics-based approaches to develop a time- and temperature-dependent model of ASR, which is eventually used in generating user-friendly charts to conveniently assess existing concrete structures. To reach a higher degree of confidence in the precision of the model, an experimental dataset was developed from the laboratory and was combined with a dataset from the literature. A comparison between the developed model and a chemo-mechanical one (Gao's model) showed higher accuracy for the developed model. This higher accuracy was more obvious regarding the specimen with fine single-size aggregate grading. This study also reveals a varying thickness of connected porosity (t_c) for fine single-size aggregate. Based on the results, aggregate size and t_c have a coupled effect on the ASR-induced expansion.